Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Tensor-based blind signal recovery for multi-carrier amplify-and-forward relay networks

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Cooperative communications have great potentials in performance enhancement via deploying relay nodes. However, these kinds of benefits usually come at the cost of more system parameters to be estimated. This fact definitely reduces the efficiency of wireless systems and then it motivates the research on the blind algorithms for cooperative communications that need fewer parameters. In this paper, we investigate the parallel factors (PARAFAC) decomposition-based blind signal recovery algorithm design for two-hop amplify-and-forward (AF) multi-carrier cooperative networks. In particular, the uniqueness of the PARAFAC decomposition used in the proposed algorithm is first investigated in detail, and then the performance of signal recovery is analyzed. Finally, the simulation results assess the performance of our proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Xing C W, Ma S D, Wu Y C. Robust joint design of linear relay precoder and destination equalizer for dual-hop amplify-and-forward MIMO relay systems. IEEE Trans Signal Process, 2010, 58: 2273–2283

    Article  MathSciNet  Google Scholar 

  2. Xing C W, Ma S D, Wu Y C, et al. Transceiver design for dual-hop non-regenerative MIMO-OFDM relay systems under channel ulncertainties. IEEE Trans Signal Process, 2010, 58: 6325–6339

    Article  MathSciNet  Google Scholar 

  3. Edfors O, Sandell M, van de Beek J, et al. An Introduction to Orthogonal-Frequency Division Multiplexing. Research Report 1996: 16, Division of Signal Processing, Luleå University of Technology, 1996

    Google Scholar 

  4. Yang S, Belfiore J-C. Optimal space-time codes for the MIMO amplify-and-forward cooperative channel. IEEE Trans Inf Theory, 2007, 53: 647–663

    Article  MathSciNet  Google Scholar 

  5. Herdin M. A chunk based OFDM amplify-and-forward relaying scheme for 4G mobile radio systems. In: Proceedings of IEEE ICC 2006, Istanbul, 2006. 4507–4512

    Google Scholar 

  6. Kolda T G, Bader B W. Tensor decompositions and applications. SIAM Rev, 2009, 51: 455–500

    Article  MATH  MathSciNet  Google Scholar 

  7. Carroll J D, Chang J J. Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika, 1970, 35: 283–319

    Article  MATH  Google Scholar 

  8. Harshman R A. Foundations of the PARAFAC procedure: models and conditions for an ‘explanatory’ multi-modal factor analysis. UCLA Work Papers Phonetics, 1970, 16: 1–84

    Google Scholar 

  9. Sidiropoulos N D, Giannakis G B, Bro R. Parallel factor analysis in sensor array processing. IEEE Trans Signal Process, 2000, 48: 2377–2388

    Article  Google Scholar 

  10. Sidiropoulos N D, Bro R, Giannakis G B. Blind PARAFAC receivers for DS/CDMA systems. IEEE Trans Signal Process, 2000, 48: 810–823

    Article  Google Scholar 

  11. Jiang T, Sidiropoulos N D. A direct blind receiver for SIMO and MIMO OFDM systems subject to unknown frequency offset and multipath. In: Proceedings of the 4th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Rome, 2003. 358–362

    Google Scholar 

  12. Roemer F, Haardt M. Tensor-based channel estimation and iterative refinements for two-way relaying with multiple antennas and spatial reuse. IEEE Trans Signal Process, 2010, 58: 5720–5735

    Article  MathSciNet  Google Scholar 

  13. Kibangou A Y, De Almeida A L F. Distributed PARAFAC based DS-CDMA blind receiver for wireless sensor networks. In: Proceedings of IEEE 11th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Marrakech, 2010. 1–5

    Google Scholar 

  14. Kibangou A Y, De Almeida A L F. Distributed Khatri-Rao space-time coding and decoding for cooperative networks. In: Proceedings of the 19th European Signal Processing Conference (EUSIPCO), Barcelona, 2011. 809–813

    Google Scholar 

  15. Fernandes C A R, De Almeida A L F, Da Costa D B. Unified tensor modeling for blind receivers in multiuser uplink cooperative systems. IEEE Signal Process Lett, 2012, 19: 247–250

    Article  Google Scholar 

  16. Fernandes A R, De Almeida A L F, Da Costa D B. Blind receiver for amplify-and-forward cooperative diversity scheme. In: Proceedings of IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), San Francisco, 2011. 546–550

    Google Scholar 

  17. Kruskal J B. Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics. Linear Algebra Appl, 1977, 18: 95–138

    Article  MATH  MathSciNet  Google Scholar 

  18. Kruskal J B. Rank decomposition, and uniqueness for 3-way and N-way arrays. In: Coppi R, Bolasco S, eds. Multiway Data Analysis. The Netherlands: North-Holland, 1989. 7–18

    Google Scholar 

  19. Foschini G J. Layered space-time architecture for wireless communications in a fading environment when using multiple antennas. Bell Labs Tech J, 1996, 1: 41–59

    Article  Google Scholar 

  20. De Almeida A L F, Favier G, Mota J C M. Constrained tensor Modeling approach to blind multiple-antenna CDMA schemes. IEEE Trans Signal Process, 2008, 56: 2417–2428

    Article  MathSciNet  Google Scholar 

  21. Sidiropoulos N D, Budampati R S. Khatri-Rao space-time codes. IEEE Trans Signal Process, 2002, 50: 2396–2407

    Article  MathSciNet  Google Scholar 

  22. De Almeida A L F, Favier G, Mota J C M. PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization. Signal Process, 2007, 87: 337–351

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Zuo Wang, Jing Wang, ChengWen Xing, ZeSong Fei & JingMing Kuang

Authors
  1. Zuo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ChengWen Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ZeSong Fei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. JingMing Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Wang, J., Xing, C. et al. Tensor-based blind signal recovery for multi-carrier amplify-and-forward relay networks. Sci. China Inf. Sci. 57, 1–11 (2014). https://doi.org/10.1007/s11432-014-5162-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-014-5162-0

Keywords

Search

Navigation